What are some real-world examples of batch size in machine learning?

What are some real-world examples of batch size in machine learning?

Batch size refers to the number of training examples utilized in one iteration. For instance, in image classification, a batch size of 32 might be used to train a model on 32 images at once.

Can you give me more examples from different domains?

Sure! In natural language processing, a common batch size is 64 for training models on text data. In reinforcement learning, batch sizes can vary widely depending on the environment.

How does batch size affect training time and model performance?

Larger batch sizes can speed up training time due to parallel processing but may lead to poorer generalization. Smaller batches often provide better model performance but take longer to train.

What is the best batch size to use?

The best batch size often depends on the specific problem and dataset. It's common to experiment with different sizes to find the optimal one for your model.